首页|Study Data from Sun Yat-sen University Update Knowledge of Intelligent Systems (Autonomous Imaging Scheduling Networks of Small Celestial Bodies Flyby Based On Deep Reinforcement Learning)
Study Data from Sun Yat-sen University Update Knowledge of Intelligent Systems (Autonomous Imaging Scheduling Networks of Small Celestial Bodies Flyby Based On Deep Reinforcement Learning)
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NETL
NSTL
Springer Nature
Investigators discuss new findings in Machine Learning - Intelligent Systems. According to news reporting out of Shenzhen, People's Republic of China, by NewsRx editors, research stated, "During the flyby mission of small celestial bodies in deep space, it is hard for spacecraft to take photos at proper positions only rely on ground-based scheduling, due to the long communication delay and environment uncertainties. Aimed at imaging properly, an autonomous imaging policy generated by the scheduling networks that based on deep reinforcement learning is proposed in this paper." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Basic Scientific Research Project.
ShenzhenPeople's Republic of ChinaAsiaIntelligent SystemsEmerging TechnologiesMachine LearningReinforcement LearningSun Yat-sen University
NETL
NSTL
Springer Nature
